CI: Test numpy 1.24 on 3.11 build

ci/deps/actions-311.yaml

@@ -18,7 +18,7 @@ dependencies:
 
   # required dependencies
   - python-dateutil
-  - numpy<1.24.0
+  - numpy
   - pytz
 
   # optional dependencies

pandas/compat/numpy/__init__.py

@@ -9,6 +9,7 @@
 np_version_under1p21 = _nlv < Version("1.21")
 np_version_under1p22 = _nlv < Version("1.22")
 np_version_gte1p22 = _nlv >= Version("1.22")
+np_version_gte1p24 = _nlv >= Version("1.24")
 is_numpy_dev = _nlv.dev is not None
 _min_numpy_ver = "1.20.3"
 

pandas/core/arrays/masked.py

@@ -428,10 +428,14 @@ def to_numpy(
                     "for this dtype."
                 )
             # don't pass copy to astype -> always need a copy since we are mutating
-            data = self._data.astype(dtype)
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=RuntimeWarning)
+                data = self._data.astype(dtype)
             data[self._mask] = na_value
         else:
-            data = self._data.astype(dtype, copy=copy)
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=RuntimeWarning)
+                data = self._data.astype(dtype, copy=copy)
         return data
 
     @doc(ExtensionArray.tolist)
@@ -464,7 +468,10 @@ def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
         # if we are astyping to another nullable masked dtype, we can fastpath
         if isinstance(dtype, BaseMaskedDtype):
             # TODO deal with NaNs for FloatingArray case
-            data = self._data.astype(dtype.numpy_dtype, copy=copy)
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=RuntimeWarning)
+                # TODO: Is rounding what we want long term?
+                data = self._data.astype(dtype.numpy_dtype, copy=copy)
             # mask is copied depending on whether the data was copied, and
             # not directly depending on the `copy` keyword
             mask = self._mask if data is self._data else self._mask.copy()

pandas/core/arrays/timedeltas.py

@@ -7,6 +7,7 @@
     Iterator,
     cast,
 )
+import warnings
 
 import numpy as np
 
@@ -917,11 +918,20 @@ def sequence_to_td64ns(
             mask = np.isnan(data)
         # The next few lines are effectively a vectorized 'cast_from_unit'
         m, p = precision_from_unit(unit or "ns")
-        base = data.astype(np.int64)
+        with warnings.catch_warnings():
+            # Suppress RuntimeWarning about All-NaN slice
+            warnings.filterwarnings(
+                "ignore", "invalid value encountered in cast", RuntimeWarning
+            )
+            base = data.astype(np.int64)
         frac = data - base
         if p:
             frac = np.round(frac, p)
-        data = (base * m + (frac * m).astype(np.int64)).view("timedelta64[ns]")
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore", "invalid value encountered in cast", RuntimeWarning
+            )
+            data = (base * m + (frac * m).astype(np.int64)).view("timedelta64[ns]")
         data[mask] = iNaT
         copy = False
 

pandas/core/dtypes/astype.py

@@ -9,6 +9,7 @@
     TYPE_CHECKING,
     overload,
 )
+import warnings
 
 import numpy as np
 
@@ -153,7 +154,9 @@ def _astype_float_to_int_nansafe(
         # GH#45151
         if not (values >= 0).all():
             raise ValueError(f"Cannot losslessly cast from {values.dtype} to {dtype}")
-    return values.astype(dtype, copy=copy)
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", category=RuntimeWarning)
+        return values.astype(dtype, copy=copy)
 
 
 def astype_array(values: ArrayLike, dtype: DtypeObj, copy: bool = False) -> ArrayLike:

pandas/core/dtypes/cast.py

@@ -398,7 +398,11 @@ def trans(x):
         and not is_bool_dtype(result.dtype)
         and not is_string_dtype(result.dtype)
     ):
-        new_result = result.astype(dtype)
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore", "overflow encountered in cast", RuntimeWarning
+            )
+            new_result = result.astype(dtype)
 
         # Adjust tolerances based on floating point size
         size_tols = {4: 5e-4, 8: 5e-8, 16: 5e-16}
@@ -1606,7 +1610,9 @@ def maybe_cast_to_integer_array(arr: list | np.ndarray, dtype: np.dtype) -> np.n
                 )
                 casted = np.array(arr, dtype=dtype, copy=False)
         else:
-            casted = arr.astype(dtype, copy=False)
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", category=RuntimeWarning)
+                casted = arr.astype(dtype, copy=False)
     except OverflowError as err:
         raise OverflowError(
             "The elements provided in the data cannot all be "
@@ -1618,7 +1624,7 @@ def maybe_cast_to_integer_array(arr: list | np.ndarray, dtype: np.dtype) -> np.n
         return casted
 
     with warnings.catch_warnings():
-        warnings.filterwarnings("ignore")
+        warnings.filterwarnings("ignore", category=RuntimeWarning)
         if np.array_equal(arr, casted):
             return casted
 
@@ -1821,7 +1827,9 @@ def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
                 # see test_where_complex GH#6345
                 return dtype.type(element)
 
-            casted = dtype.type(element)
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore")
+                casted = dtype.type(element)
             if casted == element:
                 return casted
             # otherwise e.g. overflow see test_32878_complex_itemsize

pandas/core/indexes/base.py

@@ -3872,9 +3872,15 @@ def _get_fill_indexer(
             #  but that doesn't appear to be enforced
             # error: "IndexEngine" has no attribute "get_indexer_with_fill"
             engine = self._engine
-            return engine.get_indexer_with_fill(  # type: ignore[union-attr]
-                target=target._values, values=self._values, method=method, limit=limit
-            )
+            with warnings.catch_warnings():
+                # TODO: We need to fix this. Casting to int64 in cython
+                warnings.filterwarnings("ignore", category=RuntimeWarning)
+                return engine.get_indexer_with_fill(  # type: ignore[union-attr]
+                    target=target._values,
+                    values=self._values,
+                    method=method,
+                    limit=limit,
+                )
 
         if self.is_monotonic_increasing and target.is_monotonic_increasing:
             target_values = target._get_engine_target()

pandas/tests/extension/test_period.py

@@ -17,10 +17,13 @@
 import pytest
 
 from pandas._libs import iNaT
+from pandas.compat import is_platform_windows
+from pandas.compat.numpy import np_version_gte1p24
 
 from pandas.core.dtypes.dtypes import PeriodDtype
 
 import pandas as pd
+import pandas._testing as tm
 from pandas.core.arrays import PeriodArray
 from pandas.tests.extension import base
 
@@ -94,6 +97,14 @@ def test_combine_add(self, data_repeated):
         # Period + Period is not defined.
         pass
 
+    @pytest.mark.parametrize("periods", [1, -2])
+    def test_diff(self, data, periods):
+        if is_platform_windows() and np_version_gte1p24:
+            with tm.assert_produces_warning(RuntimeWarning, check_stacklevel=False):
+                super().test_diff(data, periods)
+        else:
+            super().test_diff(data, periods)
+
 
 class TestInterface(BasePeriodTests, base.BaseInterfaceTests):
 

pandas/tests/io/parser/test_c_parser_only.py

@@ -21,6 +21,7 @@
     IS64,
     is_ci_environment,
 )
+from pandas.compat.numpy import np_version_gte1p24
 from pandas.errors import ParserError
 import pandas.util._test_decorators as td
 
@@ -114,14 +115,16 @@ def test_dtype_and_names_error(c_parser_only):
 3.0 3
 """
     # fallback casting, but not castable
+    warning = RuntimeWarning if np_version_gte1p24 else None
     with pytest.raises(ValueError, match="cannot safely convert"):
-        parser.read_csv(
-            StringIO(data),
-            sep=r"\s+",
-            header=None,
-            names=["a", "b"],
-            dtype={"a": np.int32},
-        )
+        with tm.assert_produces_warning(warning, check_stacklevel=False):
+            parser.read_csv(
+                StringIO(data),
+                sep=r"\s+",
+                header=None,
+                names=["a", "b"],
+                dtype={"a": np.int32},
+            )
 
 
 @pytest.mark.parametrize(

pandas/tests/series/test_constructors.py

@@ -804,7 +804,7 @@ def test_constructor_floating_data_int_dtype(self, frame_or_series):
         # Long-standing behavior (for Series, new in 2.0 for DataFrame)
         #  has been to ignore the dtype on these;
         #  not clear if this is what we want long-term
-        expected = frame_or_series(arr)
+        # expected = frame_or_series(arr)
 
         # GH#49599 as of 2.0 we raise instead of silently retaining float dtype
         msg = "Trying to coerce float values to integer"
@@ -816,7 +816,7 @@ def test_constructor_floating_data_int_dtype(self, frame_or_series):
 
         # pre-2.0, when we had NaNs, we silently ignored the integer dtype
         arr[0] = np.nan
-        expected = frame_or_series(arr)
+        # expected = frame_or_series(arr)
 
         msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
         with pytest.raises(IntCastingNaNError, match=msg):